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For the classification stage, multilayer perceptron neural networks were implemented according to standard backpropagation learning formulations. We demonstrate the efficiency of our feature extraction method on data to improve the ANN detection performance. As a result, we achieved the accuracy in detection and classification of seizure EEG signals with 94.69%, which is relatively good comparing with the available algorithms at present time. <\/jats:p>","DOI":"10.1142\/s0219691310003341","type":"journal-article","created":{"date-parts":[[2010,2,25]],"date-time":"2010-02-25T02:14:11Z","timestamp":1267064051000},"page":"33-48","source":"Crossref","is-referenced-by-count":20,"title":["EPILEPTIC SPIKE DETECTION USING CONTINUOUS WAVELET TRANSFORMS AND ARTIFICIAL NEURAL NETWORKS"],"prefix":"10.1142","volume":"08","author":[{"given":"BERDAKH","family":"ABIBULLAEV","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Yeungnam University, Gyeongbuk Gyeongsan, Dae-Dong 214-1\/712-749, South Korea"}]},{"given":"HEE DON","family":"SEO","sequence":"additional","affiliation":[{"name":"Microsystem Laboratory, Yeungnam University, Gyeongbuk Gyeongsan, Dae-Dong 214-1\/712-749, South Korea"}]},{"given":"MIN SOO","family":"KIM","sequence":"additional","affiliation":[{"name":"Integrated Energy Research Institute, Dongguk University, Gyeongbuk Gyeongju, Seokjang-dong 707, 780-714, South Korea"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","volume-title":"Dictionary of Epilepsy. 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